Soil degradation and climate change are the most destructive (human- and/or naturally induced) processes, making agricultural production more challenging than ever before. Traditional tillage methods, characterized by intensive mechanical soil disturbance (dominantly using a plow), have come under question for their role in exacerbating soil erosion, depleting organic matter, and contributing to the decline in soil biodiversity and other soil devastating processes. These practices, while effective in the short term for crop production, undermine the sustainability of agricultural systems, posing a threat to food security and environmental stability. This review examines the adoption and implementation of Conservation Soil Tillage (CST) across six European countries: Croatia, Serbia, Hungary, Slovakia, Czech Republic, and Poland. The main objective is to analyze the historical development, current status, and future prospects of CST in these countries, highlighting the challenges and opportunities in transitioning from conventional tillage methods. Conservation Soil Tillage (CST) emerges as a promising alternative platform to still dominant conventional plowing tillage approach. By reducing the intensity and frequency of tillage, CST practices aim to maintain adequate soil cover, minimize erosion, and encourage biological activity and organic matter accumulation, thus, ensuring soil productivity and resilience against additional degradation and climate variation. Efforts made by scientists and the government to go over it sometimes are not sufficient. Farmers’ expectations of benefits are the final keystone for the integration of CST as a dominant sustainable practice. Analyses from six European countries pointed to a high level of diversity in readiness and willingness to accept, as well as different levels of knowledge about the adoption of CST. Our study suggested that the adoption of CST is increasing, and it represents a key strategy for soil degradation prevention and climate change mitigation.

Measuring the soil water content (SWC) is a fundamental component of the sustainable management of water resources, soil preservation, and high irrigation efficiency. Non-destructive SWC measurements using soil moisture sensors (SMSs) enables timely irrigation and reduces overirrigation and water stress. Within this context, the performance of four commercial single-point soil moisture sensors (Watermark and tensiometer (Irrometer Company, Inc., Riverside, CA, USA), SM150 (Delta-T Devices, Cambridge, UK)), FieldScout TDR300 (Spectrum Technologies, Aurora, IL, USA) and one soil profile PR2 probe (Delta-T Devices, Cambridge, UK) were tested under anthropogenic eutric cambisol with a silty clay loamy texture (20, 30, and 40 cm) to evaluate accuracy and sensitivity to changes in the SWC in an irrigated apple orchard. The Watermark and tensiometer were additionally tested in the laboratory to convert soil water tension (kPa) to the volumetric soil water content (%vol.). In general, all tested SMSs responded to changes in the SWC, with sensor-to-sensor differences. The Watermark and tensiometer underestimated the SWC, while the TDR overestimated the SWC. The SM150 and PR2 showed high accuracy, i.e., SM150—RMSE-2.24 (20 cm), 2.18 (30 cm) and 2.34 (40 cm), MSE—5.02 (20 cm), 2.93 (30 cm) and 1.89 (40 cm), and PR2—RMSE-1.8 (20 cm), 1.3 (30 cm) and 1.55 (40 cm), MSE-3.23 (20 cm), 1.7 (30 cm) and 2.39 (40 cm) at all observed soil depths.

Effects of soil tillage (CTconventional, SS-subsoiling, CH-chiselling, DH-disk-harrowing and NT-no-till) and nitrogen rate (reduced N1, optimal N2 and luxury N3) on yield and yield components of maize and winter wheat in two different agricultural subregions of Croatia (Magadenovac and Cacinci site), were studied in the years 2013–2014 as part of a long-term experiment. Maize yield and yield components were influenced by site properties, tillage and nitrogen treatments. The highest yields and yield components were recorded at the Magadenovac site with N2 and N3. The lowest values of the yield and yield components of maize were recorded on NT and were significantly lower than CT, SS, CH and DH, among which no significant differences were recorded. Winter wheat yield and yield components were affected by site properties and nitrogen rates while soil tillage treatments only had an influence on grain and straw yield and plant height. Winter wheat achieved maximum yield and yield components on a N3 and N2 and at the Magadenovac site. Winter wheat grain yield decreased in the following order: SS > DH > CH > NT > CT. The obtained results indicate the importance of optimal nitrogen fertilization and the possibility of the implementation of conservation tillage for maize and winter wheat production in different agroecological conditions.

The aim of this study was to narrow the research gap of ambiguity in which machine learning algorithms should be selected for evaluation in digital soil organic carbon (SOC) mapping. This was performed by providing a comprehensive assessment of prediction accuracy for 15 frequently used machine learning algorithms in digital SOC mapping based on studies indexed in the Web of Science Core Collection (WoSCC), providing a basis for algorithm selection in future studies. Two study areas, including mainland France and the Czech Republic, were used in the study based on 2514 and 400 soil samples from the LUCAS 2018 dataset. Random Forest was first ranked for France (mainland) and then ranked for the Czech Republic regarding prediction accuracy; the coefficients of determination were 0.411 and 0.249, respectively, which was in accordance with its dominant appearance in previous studies indexed in the WoSCC. Additionally, the K-Nearest Neighbors and Gradient Boosting Machine regression algorithms indicated, relative to their frequency in studies indexed in the WoSCC, that they are underrated and should be more frequently considered in future digital SOC studies. Future studies should consider study areas not strictly related to human-made administrative borders, as well as more interpretable machine learning and ensemble machine learning approaches.

At present there is little information available concerning the relationship between the tillage system applied and the soil moisture tension, penetration resistance, soil moisture content, and temperature responses to the temporal variability of precipitation under the semi-arid Mediterranean...